DDF-ISM:使用概率定向距离场对人类头部内部结构建模
IEEE transactions on visualization and computer graphics
|March 3, 2025
概括
这项研究引入了一种新方法,用于为数字化身创建精确的3D内部头部模型. 用于内部结构建模的定向距离场 (DDF-ISM) 提高了效率和解剖学精度.
科学领域:
- 计算机图形 计算机图形
- 医疗成像医学成像
- 计算解剖学的计算解剖学
背景情况:
- 准确的3D人头模型对于数字化身和模拟至关重要.
- 目前使用显式表示 (点云,网格) 的方法在计算上效率低下,并且难以处理局部几何细节.
- 需要超越外部近似的先进的内部建模技术.
研究的目的:
- 提出一种新的方法,用于内部结构建模的定向距离场 (DDF-ISM),用于高效和解剖学准确的3D内部人类头部建模.
- 克服现有的显式建模方法的局限性.
- 为了促进内部头部结构的精确变形.
主要方法:
- 开发了DDF-ISM,这是一种利用概率定向距离场 (DDF) 来实现隐式表示的新型建模方法.
- 实现了一个概率DDF网络,以捕获本地几何信息.
- 利用受条件变形网络,以局部几何学为指导,以达到解剖学准确度.
- 从CT扫描和模板模型 (头骨,下,大脑,头部表面) 创建了一个大规模的人类头部数据集,其内部结构来自CT扫描和模板模型.
主要成果:
- 在建模质量方面,DDF-ISM与现有方法相比,表现优越.
- 提出的方法在生成3D内部头部模型方面实现了更高的效率.
- 对大规模数据集的评估证实了DDF-ISM在捕获复杂的局部几何特征方面的有效性.
结论:
- DDF-ISM为数字应用提供了3D内部人类头部建模的重大进步.
- 隐式表示和引导变形方法提高了准确性和计算效率.
- 开发的数据集为该领域的未来研究提供了宝贵的资源.
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